


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{'vocab_file': 'spiece.model'}

__UpperCamelCase     		=							{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

__UpperCamelCase     		=							{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
__UpperCamelCase     		=							0
__UpperCamelCase     		=							1
__UpperCamelCase     		=							2
__UpperCamelCase     		=							3
__UpperCamelCase     		=							4







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Optional[int] 			=		VOCAB_FILES_NAMES
			_UpperCamelCase		:       Dict 			=		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:       Optional[int] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:       Optional[int] 			=		'left'




			def __init__(				self   ,    snake_case   ,    snake_case=False   ,    snake_case=True   ,    snake_case=False   ,    snake_case="<s>"   ,    snake_case="</s>"   ,    snake_case="<unk>"   ,    snake_case="<sep>"   ,    snake_case="<pad>"   ,    snake_case="<cls>"   ,    snake_case="<mask>"   ,    snake_case=["<eop>", "<eod>"]   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else mask_token

						UpperCamelCase__       =							{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    do_lower_case=snake_case   ,    remove_space=snake_case   ,    keep_accents=snake_case   ,    bos_token=snake_case   ,    eos_token=snake_case   ,    unk_token=snake_case   ,    sep_token=snake_case   ,    pad_token=snake_case   ,    cls_token=snake_case   ,    mask_token=snake_case   ,    additional_special_tokens=snake_case   ,    sp_model_kwargs=self.sp_model_kwargs   ,    **snake_case   ,    )

						UpperCamelCase__       =							3

						UpperCamelCase__       =							do_lower_case
						UpperCamelCase__       =							remove_space
						UpperCamelCase__       =							keep_accents
						UpperCamelCase__       =							vocab_file

						UpperCamelCase__       =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(snake_case  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return len(self.sp_model  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							{self.convert_ids_to_tokens(snake_case  ): i for i in range(self.vocab_size  )}
						vocab.update(self.added_tokens_encoder  )
						return vocab




			def __getstate__(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.__dict__.copy()
						UpperCamelCase__       =							None
						return state




			def __setstate__(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							d

						# for backward compatibility
						if not hasattr(self   ,    "sp_model_kwargs"  ):
									UpperCamelCase__       =							{}

						UpperCamelCase__       =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(self.vocab_file  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if self.remove_space:
									UpperCamelCase__       =							" ".join(inputs.strip().split()  )
						else:
									UpperCamelCase__       =							inputs
						UpperCamelCase__       =							outputs.replace("``"   ,    "\""  ).replace("''"   ,    "\""  )

						if not self.keep_accents:
									UpperCamelCase__       =							unicodedata.normalize("NFKD"   ,    snake_case  )
									UpperCamelCase__       =							"".join([c for c in outputs if not unicodedata.combining(snake_case  )]  )
						if self.do_lower_case:
									UpperCamelCase__       =							outputs.lower()

						return outputs




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.preprocess_text(snake_case  )
						UpperCamelCase__       =							self.sp_model.encode(snake_case   ,    out_type=snake_case  )
						UpperCamelCase__       =							[]
						for piece in pieces:
									if len(snake_case  ) > 1 and piece[-1] == str(","  ) and piece[-2].isdigit():
												UpperCamelCase__       =							self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case   ,    ""  )  )
												if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
															if len(cur_pieces[0]  ) == 1:
																		UpperCamelCase__       =							cur_pieces[1:]
															else:
																		UpperCamelCase__       =							cur_pieces[0][1:]
												cur_pieces.append(piece[-1]  )
												new_pieces.extend(snake_case  )
									else:
												new_pieces.append(snake_case  )

						return new_pieces




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return self.sp_model.PieceToId(snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return self.sp_model.IdToPiece(snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							"".join(snake_case  ).replace(snake_case   ,    " "  ).strip()
						return out_string




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = False   ,    snake_case = None   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							kwargs.pop("use_source_tokenizer"   ,    snake_case  )

						UpperCamelCase__       =							self.convert_ids_to_tokens(snake_case   ,    skip_special_tokens=snake_case  )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						UpperCamelCase__       =							[]
						UpperCamelCase__       =							[]
						for token in filtered_tokens:
									if skip_special_tokens and token in self.all_special_ids:
												continue
									if token in self.added_tokens_encoder:
												if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(snake_case  )  )
															UpperCamelCase__       =							[]
												sub_texts.append(snake_case  )
									else:
												current_sub_text.append(snake_case  )
						if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(snake_case  )  )

						# Mimic the behavior of the Rust tokenizer:
						# By default, there are no spaces between special tokens
						UpperCamelCase__       =							"".join(snake_case  )

						UpperCamelCase__       =							(
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
									UpperCamelCase__       =							self.clean_up_tokenization(snake_case  )
									return clean_text
						else:
									return text




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]
						if token_ids_a is None:
									return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = False  ):

						'''simple docstring'''


						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=snake_case   ,    token_ids_a=snake_case   ,    already_has_special_tokens=snake_case  )

						if token_ids_a is not None:
									return ([0] * len(snake_case  )) + [1] + ([0] * len(snake_case  )) + [1, 1]
						return ([0] * len(snake_case  )) + [1, 1]




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[2]

						if token_ids_a is None:
									return len(token_ids_a + sep  ) * [0] + cls_segment_id
						return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						if not os.path.isdir(snake_case  ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
						UpperCamelCase__       =							os.path.join(
						    snake_case   ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

						if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case  ) and os.path.isfile(self.vocab_file  ):
									copyfile(self.vocab_file   ,    snake_case  )
						elif not os.path.isfile(self.vocab_file  ):
									with open(snake_case   ,    "wb"  ) as fi:
												UpperCamelCase__       =							self.sp_model.serialized_model_proto()
												fi.write(snake_case  )

						return (out_vocab_file,)






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


